Dynamic Multi-modal Fusion for Biosignal-based Motion Sickness Prediction in Vehicles
These videos have been matched automatically. Contact us if they are not relevant.
These videos have been matched automatically. Contact us if they are not relevant.
View abstract on PubMed
Summary
This summary is machine-generated.Autonomous vehicles present motion sickness challenges. A new dynamic multi-modal fusion framework effectively classifies motion sickness by adaptively focusing on relevant biosignals in real-world driving.
Area Of Science
- Automotive Engineering
- Biomedical Engineering
- Human-Computer Interaction
Background
- Motion sickness (MS) is a significant challenge for autonomous vehicle (AV) adoption and passenger comfort.
- Traditional MS research methods lack real-world complexity and robust data analysis capabilities.
- Multi-biosignal fusion offers a promising approach to capture complex physiological responses related to MS.
Purpose Of The Study
- To introduce a novel dynamic multi-modal fusion for motion sickness classification (DMFMS) framework.
- To address the limitations of traditional MS detection in noisy, real-world driving environments.
- To enhance the accuracy and reliability of MS prediction in autonomous vehicles.
Main Methods
- Developed a DMFMS framework incorporating confidence-aware learning for modality reliability estimation.
- Implemented a dynamic gating mechanism to adaptively weigh modality contributions.
- Integrated a spatial-temporal attention module (STAM) to focus on relevant biosignal information.
- Conducted experiments using a multi-biosignal dataset from 13 subjects in real driving scenarios.
Main Results
- The DMFMS framework demonstrated superior performance compared to conventional MS prediction models.
- Adaptive focusing on significant samples and noise evaluation improved classification accuracy.
- Confidence-aware learning and dynamic gating effectively managed noisy multi-modal data.
- The STAM successfully filtered extraneous information, enhancing focus on critical biosignals.
Conclusions
- The proposed DMFMS framework represents a superior solution for detecting motion sickness in real-world autonomous driving.
- Dynamic multi-modal fusion effectively handles the complexities of biosignal data in noisy environments.
- This approach enhances passenger comfort and safety in autonomous vehicles.
These videos have been matched automatically. Contact us if they are not relevant.
These videos have been matched automatically. Contact us if they are not relevant.
